Combining Accumulated Frame Differencing and Corner Detection for Motion Detection

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dc.contributor.authorAlgethami, Nahlahen_US
dc.contributor.authorRedfern, Samen_US
dc.contributor.editor{Tam, Gary K. L. and Vidal, Francken_US
dc.date.accessioned2018-09-19T15:15:01Z
dc.date.available2018-09-19T15:15:01Z
dc.date.issued2018
dc.description.abstractDetecting and tracking people in a meeting room is very important for many applications. In order to detect people in a meeting room with no prior knowledge (e.g. background model) and regardless of whether their motion is slow or significant, this paper proposes a coarse-to-fine people detection algorithm by combining a novel motion detection process, namely, adaptive accumulated frame differencing (AAFD) combined with corner features. Firstly, the region of movement is extracted adaptively using AAFD, then motion corner features are extracted. Finally, the minimum area rectangle fitting these corners is found. The proposed algorithm is evaluated using the AMI meeting data set and this indicates promising results for people detection.en_US
dc.description.sectionheadersVision and Learning
dc.description.seriesinformationComputer Graphics and Visual Computing (CGVC)
dc.identifier.doi10.2312/cgvc.20181202
dc.identifier.isbn978-3-03868-071-0
dc.identifier.pages7-14
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/cgvc20181202
dc.identifier.urihttps://doi.org/10.2312/cgvc.20181202
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectTracking
dc.subjectMotion capture
dc.titleCombining Accumulated Frame Differencing and Corner Detection for Motion Detectionen_US
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